When the capacity of a hypothesis class \({\mathcal {H}}\)  is below the interpolation threshold, not enough to fit arbitrary data,
learned predictors follow the classical U-curve from Figure REF .
The shape of the generalization curve undergoes a qualitative change when the capacity of \({\mathcal {H}}\)  passes the  interpolation threshold, i.e., becomes large enough to interpolate the data.
Although predictors at the interpolation threshold typically have high risk, further increasing the number of parameters (capacity of \({\mathcal {H}}\) ) leads to improved generalization.
The double descent pattern has been empirically demonstrated for a broad range of datasets and algorithms, including modern deep neural networks [1]}, [2]}, [3]} and observed earlier for linear models [4]}. The “modern” regime of the curve, the phenomenon that large number of parameters often do not lead to over-fitting has historically been observed in boosting [5]}, [6]} and random forests, including interpolating random forests [7]} as well as in neural networks [8]}, [9]}.
